Predicting Wireless MmWave Massive MIMO Channel Characteristics Using Machine Learning Algorithms

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2018
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Abstract
This paper proposes a procedure of predicting channel characteristics based on a well-known machine learning (ML) algorithm and convolutional neural network (CNN), for three-dimensional (3D) millimetre wave (mmWave) massive multiple-input multiple-output (MIMO) indoor channels. The channel parameters, such as amplitude, delay, azimuth angle of departure (AAoD), elevation angle of departure (EAoD), azimuth angle of arrival (AAoA), and elevation angle of arrival (EAoA), are generated by a ray tracing software. After the data preprocessing, we can obtain the channel statistical characteristics (including expectations and spreads of the above-mentioned parameters) to train the CNN. The channel statistical characteristics of any subchannels in a specified indoor scenario can be predicted when the location information of the transmitter (Tx) antenna and receiver (Rx) antenna is input into the CNN trained by limited data. The predicted channel statistical characteristics can well fit the real channel statistical characteristics. The probability density functions (PDFs) of error square and root mean square errors (RMSEs) of channel statistical characteristics are also analyzed.
Reference Key
bai2018predictingwireless Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Bai, Lu;Wang, Cheng-Xiang;Huang, Jie;Xu, Qian;Yang, Yuqian;Goussetis, George;Sun, Jian;Zhang, Wensheng;
Journal wireless communications and mobile computing
Year 2018
DOI
DOI not found
URL
http://dx.doi.org/10.1155/2018/9783863
Keywords Keywords not found

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